His et al. BMC Medicine(2021) 19:312
https://doi.org/10.1186/s12916-021-02183-2

RESEARCH ARTICLE

Open Access

Lifestyle correlates of eight breast cancerrelated metabolites: a cross-sectional study
within the EPIC cohort
Mathilde His1, Vivian Viallon1, Laure Dossus1, Julie A. Schmidt2, Ruth C. Travis2, Marc J. Gunter1, Kim Overvad3,
Cecilie Kyrø4, Anne Tjønneland4,5, Lucie Lécuyer6, Joseph A. Rothwell6, Gianluca Severi6,7, Theron Johnson8,
Verena Katzke8, Matthias B. Schulze9,10, Giovanna Masala11, Sabina Sieri12, Salvatore Panico13, Rosario Tumino14,
Alessandra Macciotta15, Jolanda M. A. Boer16, Evelyn M. Monninkhof17, Karina Standahl Olsen18, Therese H. Nøst18,
Torkjel M. Sandanger18, Antonio Agudo19,20, Maria-Jose Sánchez21,22,23,24, Pilar Amiano25,26,27,
Sandra M. Colorado-Yohar27,28,29, Eva Ardanaz27,30,31, Linda Vidman32, Anna Winkvist33, Alicia K. Heath34,
Elisabete Weiderpass35, Inge Huybrechts1 and Sabina Rinaldi1*

Abstract
Background: Metabolomics is a promising molecular tool for identifying novel etiological pathways leading tocancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones,
we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite(acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine,
phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2).
Methods: To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional
analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included ascontrols in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition
cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with
42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant
associations were evaluated in a validation set (n = 786).
Results: For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:
2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total andsaturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively
associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively
associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors
examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive
association with age was observed.

* Correspondence: rinaldis@iarc.fr
1
International Agency for Research on Cancer (IARC/WHO), Nutrition andMetabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08 Lyon, France
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His et al. BMC Medicine(2021) 19:312

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Conclusions: These associations may indicate possible mechanisms underlying associations between lifestyle andanthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestylecorrelates of the metabolites investigated.
Keywords: Metabolites, Breast cancer, Cross-sectional, Lifestyle, Anthropometry

Background
Metabolomics is an important tool in the identificationof new etiological pathways associated with chronic diseases, including breast cancer [1–8], as the metabolome
reflects both endogenous parameters and exogenous exposures [9]. Prospective studies using targeted metabolomics (analyses of a pre-defined panel of metabolites) or
untargeted metabolomics approaches have reported
novel associations of pre-diagnostic blood concentrations of endogenous metabolites with breast cancer risk.
These metabolites include lysophosphatidylcholine aC18:0 [8], 16a-hydroxy-DHEA-3-sulfate [4, 5], various
carnitines [4, 5], caprate (10:0) [6], histidine, glycerol, Nacetyl-glycoprotein [7], acetone, glycerol-derived compounds, other amino acids, and lipids [2, 3], suggesting
new potential avenues of research and possible additional targets for prevention.
In a previous case-control study nested within theEuropean Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the association between blood concentrations of endogenous metabolites,
measured by targeted metabolomics, and risk of breast
cancer [1]. We reported a positive association between
acetylcarnitine (C2) and breast cancer risk and negative
associations of arginine, asparagine, phosphatidylcholines acyl-alkyl (PCs ae) C36:3, C34:2, C36:2, C38:2, andphosphatidylcholine diacyl (PC aa) C36:3 with breast
cancer risk, among women not using exogenous hormones at blood collection.
To further assess how these findings can inform breast
cancer prevention research, a better understanding ofpotentially modifiable determinants of blood levels ofthese metabolites is needed. Towards this aim, we report
here the results of a cross-sectional analysis nested in
the EPIC cohort to investigate associations of a wide
range of lifestyle and anthropometric variables and acetylcarnitine, arginine, asparagine, PCs aa C36:3, ae C34:2,
ae C36:2, ae C36:3, and ae C38:2.
Methods
The EPIC study

EPIC is an ongoing multi-center cohort study including
approximately 520,000 participants recruited between
1992 and 2000 from ten European countries [10]. Female participants (n = 367,903) were aged 35–75 years at
recruitment. Detailed information was collected on dietary, lifestyle, reproductive, medical, and anthropometricdata at inclusion [10]. Around 246,000 women from all
countries provided a baseline blood sample. Blood wascollected according to a standardized protocol in France,
Germany, Greece, Italy, the Netherlands, Norway, Spain,
and the UK [10]. Serum (except in Norway), plasma,
erythrocytes, and buffy coat aliquots were stored in liquid nitrogen (−196°C) in a centralized biobank at IARC.
In Denmark, blood fractions were stored locally in thevapor phase of liquid nitrogen containers (−150°C), andin Sweden, they were stored locally at −80°C in standard
freezers. All participants provided written informed consent to participate in the EPIC study. This study was approved by the ethics committee of the International
Agency for Research on Cancer (IARC) and all centers.

Study population and cross-sectional design

This study included all female EPIC participants (1) who
provided a blood sample; (2) who were previously included in one of six case-control studies on cancer etiology nested within the EPIC cohort (on breast [1],endometrial [11], colorectal [12], kidney [13], liver [14],and gallbladder cancers) with available blood concentrations of acetylcarnitine, arginine, asparagine, PCs aa C36:
3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2 measured
by the same targeted metabolomics approach; (3) who
were included as control participants in these studies(i.e., free of cancer (except non-melanoma skin cancer)
at the time of the diagnosis of the cases, using
incidence-density sampling, and matched to cases byage, sex, study center, time of blood collection, fasting
status at blood collection (except for kidney cancer
study), menopausal status and exogenous hormone use
at blood collection (for breast, endometrial, liver, andgallbladder studies), and phase of menstrual cycle (forbreast and endometrial cancer studies)); and (4) whose
samples were included in an analytical batch including
at least 10 samples, to ensure proper normalization of
metabolite concentrations (see the “Statistical analyses”
section) (N = 3163).
We then excluded women who declared use of hormones at blood collection (n = 768), and those whose
hormone use status at blood collection was unknown (n= 37), because associations between the studied metabolites and breast cancer risk were limited to hormone
non-users [1]. The current analysis included data from2358 participants.

His et al. BMC Medicine(2021) 19:312

The 2358 participants were split into a discovery set(N = 1572, 66.7% of the population) and a validation set(N = 786, 33.3% of the population). Metabolites of interest were those found to be associated with breast cancer
risk, and this observed association could result from associations between metabolites and some of the correlates under study in the present work. Thus, thediscovery set included all controls from the breast cancer study (n = 1079), and randomly selected controls
from the other nested case-control studies (n = 493),
while the validation set did not include participants fromthe breast cancer study. This way, associations identified
on the discovery set and further validated on the validation set are guaranteed not to be driven by the breast
cancer study only.
Laboratory measurements

Before exclusions of hormone users, a total of 3179 samples were available for 3163 women. All samples, plasma(in 95.1% of samples) or serum, were assayed by liquid
chromatography-mass spectrometry using the AbsoluteIDQ p180 commercial kit (Biocrates Life Sciences AG,
Innsbruck, Austria). A total of 2289 (72.0%) samples
were assayed at the laboratory of the Biomarkers Groupat IARC (breast, colorectal, kidney, and liver cancer
studies); 851 (26.8%) at the Imperial College, London;and 39 (1.2%) at the Helmholtz Zentrum, München,
Germany. At IARC, analyses were run on a QTRAP5500(breast, kidney, and liver cancer studies) and TQ4500(colorectal cancer study) mass spectrometers (AB Sciex,
Framingham, MA, USA), while at the Imperial College
London and Helmholtz Zentrum, analyses were runusing an API4000TQ (endometrial and gallbladder cancer studies). All analyses for a given study were performed using the same instrument. Sixteen participants
had their samples analyzed in two different studies, at
IARC and at the Helmholtz Zentrum, for whom themetabolite concentrations were averaged over the two
measures.
Out of the 3179 samples, arginine concentrations
could not be quantified in five, as they were below thelower limit of quantification (LLOQ) and were therefore
imputed to half this LLOQ, consistently with previous
work [1].
Covariate data

Details of data collection in EPIC are described elsewhere [10]. Lifestyle and medical factors were assessedin the baseline questionnaire. Usual dietary intakes wereassessed using center- or country-specific validated
questionnaires covering the previous 12 months andmatched to the US Department of Agriculture foodcomposition database to estimate macronutrient intakes[15]. Glycemic index and glycemic load were computed.

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In all EPIC centers, except France, Oxford, and Norway,height, weight, and waist and hip circumference were
measured on all participants using similar protocols (inUmeå (Sweden), only weight and height were measured).
In France and Oxford, weight, height, and waist and hip
circumferences were measured in a sub-set of participants, but self-reported weight and height were obtained
from all individuals, and validation studies showed highcorrelations between self-reported and measured values(r ≥ 0.90) [16, 17]. In Oxford, self-reported measurements also included waist and hip circumferences. In
Norway, only self-reported height and weight wereavailable.
Dietary data were used to compute the inflammatory
score of the diet (ISD) [18] (reflecting the inflammatory
potential of the diet based on 28 dietary components),
the modified Mediterranean diet score [19] (a 9component score indicating the degree of adherence tothe traditional Mediterranean diet; 0 minimal adherence
to 9 maximal adherence), and the Diet Quality IndexInternational (DQI-I; a 17-component score based on
general nutritional guidelines [20, 21]; 0 to 100, minimal
to maximal diet quality). Dietary and lifestyle data werecombined to calculate the Healthy Lifestyle Index (HLI)[22], designed to reflect five components of lifestyle factors (smoking, alcohol consumption, diet (cereal fibers,
red and processed meat, the ratio of polyunsaturated tosaturated fatty acids, margarine, glycemic load, and fruits
and vegetables), physical activity, and body mass index;
ranging from 0, least healthy, to 20). Furthermore, we
calculated the World Cancer Research Fund/American
Institute for Cancer Research score, which reflects recommendations for cancer prevention on weight maintenance, physical activity, intake of food and drinks
which promote weight gain, of plant-based foods, ofanimal-based foods, of alcohol, and breastfeeding [23](from 0, low adherence to recommendation, to 7 forwomen).

Statistical analyses
Normalization of metabolite concentrations

A specific statistical pipeline was developed [24] and applied on raw metabolite concentrations (before exclusionof hormone users) to adequately pool measures obtained
from different studies, instruments, and laboratories.
This pipeline was shown to be efficient in removing unwanted variability and improving the comparability ofmeasurements acquired across different nested studies.
Log-transformed concentrations of the metabolites ofinterest were normalized to remove effects of analyticalbatch and study, which were estimated as random effects
in mixed-effects linear models correcting for possible
heteroscedasticity. Corrected metabolite concentrations

His et al. BMC Medicine(2021) 19:312

analyzed in this work correspond to residuals from themodel.
Missing data

When missing values on covariates represented less than
5% of the overall values, they were imputed to the mode
value (categorical variables: number of full-term pregnancies, ever use of oral contraceptive, ever use of hormones for menopause (by menopausal status), educationlevel, physical activity, smoking status, fasting status) or
median (continuous variables: age at menarche, age atfirst full-term pregnancy (among parous women), duration of breastfeeding among women who breastfed,
waist circumference, hip circumference, waist/hip ratio,time at blood collection). When missing values represented more than 5% of values for a variable, this variable was categorized, and a “missing” category wascreated (phase of menstrual cycle at blood collection forpre- and perimenopausal women, breastfeeding, lifetime
alcohol consumption, Healthy Lifestyle Index, WCRF/AICR score).
Identification of correlates

Participants’ characteristics were described using frequencies for categorical variables and mean (standard
deviation) for continuous variables. We calculated partial
Pearson’s correlations between metabolite concentrations (adjusted for center and age) and between metabolites and age (adjusted for center).
Analyses were first run in the discovery set. For eachmetabolite of interest and each lifestyle variable, a linear
regression model was built with metabolite concentration as a dependent variable. Models were adjusted forcenter of recruitment, age at blood collection, menopausal status (premenopausal, perimenopausal, postmenopausal [25]), phase of the menstrual cycle forpremenopausal women (follicular, ovulatory, luteal,
missing), time of the day, and fasting status at blood collection (“No”: < 3 h since last meal (< 4 h in Umeå), “In
between”: 3–6 h (4–8 h in Umeå), and “Yes”: > 6 h (> 8 hin Umeå)). Models that examined age as exposure were
not adjusted for age, and models with menopausal status
as main exposure were not adjusted for phase of menstrual cycle, as this variable is defined in premenopausal
women only.
Variables tested as possible correlates were age atblood collection (continuous), age at menarche (continuous), total duration of menstrual cycles (quartiles/missing), pregnancy (ever/never), number of full-term
pregnancies (continuous), age at first full-term pregnancy (nulliparous/quartiles), breastfeeding (ever/never/missing), duration of breastfeeding (nulliparous/quartiles/missing), use of oral contraceptive (ever/never;current users excluded), menopausal status at blood

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collection (premenopausal/perimenopausal/postmenopausal), use of hormones for menopause (ever/never;current users are excluded), education level (no schooling or primary/technical, professional or secondary/longer education), physical activity (Cambridge Index [26]:inactive/moderately inactive/moderately active/active),smoking status (never/former/current), smoking status
combined with intensity (never/current, 1–15 cigarettes/
day/current, 16+ cigarettes/day/current, pipe/cigar/occasional/former, quit for ≤10 years/former, quit 11–20
years/former, quit > 20 years), baseline alcohol consumption (continuous, g/day), lifetime alcohol consumption(non-drinker/former drinker/current > 0–3 g/day/> 3–
12 g/day/> 12–24 g/day/> 24 g/day/missing), BMI (continuous, kg/m2), waist circumference (continuous, cm),
hip circumference (continuous, cm), waist/hip ratio(continuous), height (continuous, cm), total energy intake (continuous, kcal/day), and the following food components estimated as residuals on total energy intake(continuous, g/day): protein, carbohydrate, starch, sugar,
fiber, fat (total), fatty acids (monounsaturated, polyunsaturated, saturated, trans, trans-monoenoic, transpolyenoic), glycemic index (continuous), glycemic load(continuous), Healthy Lifestyle Index (0–10/11–15/16–20), WCRF/AICR score (quartiles/missing), modified
Mediterranean diet score (continuous), diet quality index(continuous), and inflammatory score of the diet(continuous).
For each metabolite, P-values from F-tests for eachvariable were collected and were corrected for multiple
testing by controlling for family-wise error rate at α =0.05 by permutation-based stepdown minP adjustmentof P-values, a method which accounts for dependencies
between tests [27].
Validation

All statistically significant associations in the discoveryset (based on P-values corrected for multiple tests ≤0.05)
were assessed in the validation set, using the same modeland categories of variables as in the discovery set. In thisvalidation set, a more conservative approach was chosen
for controlling for multiple tests [28], i.e., the Bonferroni
correction based on the number of tests run for eachmetabolite.
For all variables showing a significant association withthe metabolites of interest in both the discovery and validation sets, continuous variables were categorized(quartiles) and means of metabolites, with 95% confidence intervals, were estimated in each category, using
the overall dataset (n = 2358).
Interactions

For each metabolite and each variable examined as potential correlate, we investigated interaction with fastingHis et al. BMC Medicine(2021) 19:312

status (no/in between/yes), menopausal status at bloodcollection (pre-/peri-/postmenopausal), and BMI (18.5–
24.9/25–29.9/≥30 kg/m2, excluding n = 15 participants
with BMI < 18.5 kg/m2), in the discovery set. To do so,an interaction term was added in the model and the Pvalue associated with this term was evaluated, after correction for multiple testing using the permutation minP
algorithm.
Sensitivity analyses

We conducted sensitivity analyses (1) excluding participants from the liver and gallbladder studies (n = 128),
for which the blood fraction analyzed was serum andnot plasma, and (2) excluding participants with selfreported diabetes (n = 71) or with missing data on diabetes status (n = 160) at recruitment.

Results
Participants’ characteristics overall and from the discovery and validation sets are shown in Table 1. Overall,
39.7% of the participants were not fasting at blood collection while 44.4% were considered fasting (more than
6 h since last meal (8 h in Umeå)). Around 30% of participants were premenopausal. Overall, participant characteristics were similar among discovery and validation
sets (Table 1). Of note, the mean age (standard deviation(SD)) at blood collection in the validation set was 55.5
(8.1) years and 53.1 (8.6) years in the discovery set. Consequently, the proportion of postmenopausal women
was 61.8% in the validation set and 51.4% in the discovery set. In the validation set, 42.0% of participants had
ever used oral contraceptive (vs 50.3% in the discovery
set), 53.3% of women had received none or primary education (vs 47.3% in discovery set), 29.9% were physicallyinactive (vs 24.7% in discovery set), 16.9% were current
smokers (vs 21.6% in discovery set), and 26.3% were alcohol non-consumers (vs 19.2% in discovery set).
In all participants (N = 2358), strong correlations wereobserved between acyl-alkyl PCs (Fig. 1, Pearson’s correlation coefficients 0.61 to 0.92), while moderate correlations were observed between acyl-alkyl PCs and PC aa
C36:3 (0.41 to 0.55). Arginine was moderately correlatedwith all metabolites except for acetylcarnitine (C2), withan observed correlation of 0.19 with asparagine and correlations ranging from 0.11 to 0.13 with PCs. Asparagine
showed similar low correlations (0.12 to 0.15) with PCs
and a negative correlation with C2 (−0.17). C2 showed
the greatest correlation with age (0.23), followed by PC
aa C36:3 (0.19), while for other metabolites correlations
with age ranged from −0.09 to 0.07.
In the discovery set, 104 associations (31% of the 336
associations tested, 8 metabolites × 42 variables) had Pvalues ≤0.05 (Supplementary Table 1, see Additional file1). After correction of P-values for multiple testing, 57

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of these associations remained significant (Table 2),
which did not include any associations with arginine.
Thirty associations were replicated in the validation set(same direction as in the discovery set, Supplementary
Table 1, see Additional file 1) after Bonferroni correctionof P-values, which did not include any associations with
PC aa C36:3 (Table 2).
Figure 2 represents means of the metabolite concentrations across categories of variables in the overall
population (n = 2358), for metabolites and variables forwhich a significant association was detected in both thediscovery and validation sets. Asparagine concentration
was negatively associated with BMI, waist and hip circumferences, and WHR. C2 was positively associatedwith age but not with the other factors. PCs ae C36:2
and ae C38:2 were negatively associated with BMI, waist
and hip circumferences, and waist/hip ratio. Negative associations with BMI, waist circumference, and waist/hip
ratio were also observed for PCs ae C34:2 and ae C36:3.
PC ae C34:2, C36:2, and 36:3 were additionally positively
associated with total fat intake, and with saturated fatty
acid intake, which was also positively associated with PC
ae C38:2. For PC ae C36:2, additional associations wereobserved with alcohol intake at recruitment and overlifetime (negative) and with HLI and WCRF/AICR score(positive).
Analyses of interactions with BMI, menopausal, andfasting status (Supplementary Table 2, see Additional file1) did not suggest any significant interaction with thesevariables in the associations reported above. The only interactions with significant P-values after correction for
multiple testing were with menopausal status for the association between asparagine and age (P-int = 0.04) andwith fasting status for the association of height and PC
ae C38:2 (P-int = 0.03).
When excluding serum samples (restricting the analysis to plasma samples) from both discovery (n = 40)
and validation (n = 88) sets, results were largely consistent with those in the main analyses (data not shown),
except for generally larger P-values (due to the lower
statistical power) that led to the following nonsignificant associations in the discovery set: asparagine
and WCRF/AICR score, PC aa C36:3 and age and BMI,
and PC ae C38:2 and trans-polyenoic fatty acid intake.
In the validation set, associations between asparagine
and hip and waist circumferences were not statisticallysignificant anymore. However, estimates were very closein direction and magnitude to the ones obtained overall(before exclusion of serum samples).
After exclusion of participants with self-reported diabetes at blood collection (discovery set, n = 45; validation set, n = 26) or with missing information ondiabetes (discovery set, n = 86; validation set, n = 74), associations were very similar in direction and magnitude

His et al. BMC Medicine(2021) 19:312

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Table 1 Main characteristics of women included (hormone non-users only), overall and in discovery and validation sets
Overall (n = 2358)

Discovery (n = 1572)

Validation (n = 786)

53.9 (8.5)

53.1 (8.6)

55.5 (8.1)

No

936 (39.7)

639 (40.6)

297 (37.8)

In between

375 (15.9)

252 (16.0)

123 (15.6)

Yes1047 (44.4)

681 (43.3)

366 (46.6)

Premenopausal

722 (30.6)

522 (33.2)

200 (25.4)

Postmenopausal

1294 (54.9)

808 (51.4)

486 (61.8)

Age at blood collection (years)
Fasting status at blood collectiona (%)

Menopausal status at blood collection (%)

Perimenopausal
Age at first menstrual periods (years) (mean (SD))

342 (14.5)

242 (15.4)

100 (12.7)

13.1 (1.6)

13.1 (1.6)

13.1 (1.6)

Number of full-term pregnancies (mean (SD))

2.1 (1.3)

2.0 (1.2)

2.1 (1.4)

Age at first full-term pregnancy (years) (mean (SD))

25.2 (4.3)

25.1 (4.4)

25.4 (4.2)

Yes

1669 (80.9)1110 (80.7)

559 (81.2)

No

280 (13.6)

181 (13.2)

99 (14.4)

Missing

115 (5.6)

85 (6.2)

30 (4.4)

Breastfeeding (in parous women) (%)

Ever used oral contraceptive (%)

1120 (47.5)

790 (50.3)

330 (42.0)

Ever used MHT (%)

297 (12.6)

198 (12.6)

99 (12.6)

1162 (49.3)

743 (47.3)

419 (53.3)

Education level (%)
Primary/no schoolingTechnical/professional/secondary

819 (34.7)

560 (35.6)

259 (33.0)

Longer education

377 (16.0)

269 (17.1)

108 (13.7)

Inactive623 (26.4)

388 (24.7)

235 (29.9)

Moderately inactive

929 (39.4)

623 (39.6)

306 (38.9)

Moderately active

450 (19.1)

307 (19.5)

143 (18.2)

Active356 (15.1)

254 (16.2)

102 (13.0)

Physical activity (Cambridge Index) (%)

Smoking status (%)
Never1406 (59.6)

934 (59.4)

472 (60.1)

Former480 (20.4)

299 (19.0)

181 (23.0)

Smoker472 (20.0)

339 (21.6)

133 (16.9)

Non-drinker

509 (21.6)

302 (19.2)

207 (26.3)> 0–3 g/day

707 (30.0)

482 (30.7)

225 (28.6)> 3–12 g/day

619 (26.3)

416 (26.5)

203 (25.8)> 12–24 g/day

337 (14.3)

239 (15.2)

98 (12.5)> 24 g/day

186 (7.9)

133 (8.5)

53 (6.7)

160.4 (6.8)

160.6 (6.7)

160.0 (6.8)

Alcohol consumption at recruitment (%)

Height (cm) (mean (SD))
BMI (kg/m2) (mean (SD))

26.0 (4.3)

25.9 (4.3)

26.3 (4.4)

Waist circumference (cm) (mean (SD))

81.9 (10.6)

81.5 (10.4)

82.6 (10.9)

Waist/hip ratio (mean (SD))

0.80 (0.07)

0.80 (0.07)

0.81 (0.07)

Total energy intake (kcal/day) (mean (SD))

2010.3 (547.7)

2016.3 (557.6)

1998.3 (527.4)

Healthy Lifestyle Index (mean(SD))

12.6 (3.0)

12.6 (3.0)

12.8 (2.9)

WCRF/AICR scorec (mean (SD))

3.9 (1.0)

3.9 (1.0)

3.9 (1.0)bHis et al. BMC Medicine(2021) 19:312

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Table 1 Main characteristics of women included (hormone non-users only), overall and in discovery and validation sets (Continued)
Overall (n = 2358)

Discovery (n = 1572)

Validation (n = 786)

Modified Mediterranean diet score (mean (SD))

4.3 (1.8)

4.3 (1.8)

4.5 (1.7)

Inflammatory score of the diet (mean (SD))

0.9 (1.7)

0.89 (1.7)

0.8 (1.7)

Diet Quality Index-International (mean (SD))

57.4 (7.8)

57.2 (7.9)

57.8 (7.7)

Arginine (geometric mean (SD))

64.1 (2.6)

64.2 (2.6)

63.9 (2.6)

Asparagine (geometric mean (SD))

41.5 (2.7)

41.4 (2.7)

41.6 (2.7)

C2 (geometric mean (SD))

4.9 (2.7)

4.9 (2.7)

4.9 (2.6)

PC aa C36:3 (geometric mean (SD))

130.0 (2.6)

127.0 (2.6)

136.0 (2.7)

PC ae C34:2 (geometric mean (SD))

12.2 (2.7)

12.1 (2.8)

12.4 (2.7)

PC ae C36:2 (geometric mean (SD))

15.6 (2.7)

15.5 (2.7)

15.6 (2.7)

PC ae C36:3 (geometric mean (SD))

8.1 (2.7)

8.0 (2.7)

8.2 (2.7)

PC ae C38:2 (geometric mean (SD))

2.1 (2.7)

2.1 (2.7)

2.1 (2.8)

Metabolite concentrations (normalized), μmol/L

Abbreviations: AICR American Institute for Cancer Research, BMI body mass index, C2 acetylcarnitine, MHT menopause hormone therapy, PC aa
phosphatidylcholine diacyl, PC ae phosphatidylcholine acyl-alkyl, SD standard deviation, WCRF World Cancer Research Funda
No: < 3 h since last meal (< 4 h in Umeå); in between: 3–6 h since last meal (4–8 h in Umeå); yes: > 6 h since last meal (> 8 h in Umeå)b
Healthy Lifestyle Index was missing for 144 (6.1%) participantsc
WCRF/AICR score was missing for 196 (8.3%) participants

Fig. 1 Partial Pearson correlations between metabolites identified as associated with breast cancer risk, and age (N = 2358). Metabolite concentrations
were log-transformed and normalized as described in the “Methods” section. Coefficients are shown only for significant correlations (P-value < 0.05).
Correlations between metabolite concentrations are adjusted for center and age, and correlations between metabolites and age are adjusted forcenter. Abbreviations: C2, acetylcarnitine; PC aa, phosphatidylcholine diacyl; PC ae, phosphatidylcholine acyl-alkyl

His et al. BMC Medicine(2021) 19:312

Page 8 of 15

Table 2 P-values for associations between metabolites and selecteda variables
Metabolite

VariableC2

PC aa C36:3

PC ae C34:2

PC ae C36:2

PC ae C36:3

Validation

P-valueminP P-value

P-value

Bonf. P-value

-

-

-

-

BMI

1.4E−19

1.4E−19

9.3E−075.6E−06

Waist circumference

4.8E−16

4.8E−16

9.9E−05

6.0E−04

Hip circumference

2.2E−132.2E−13

3.7E−03

2.2E−02

Waist/hip ratio

1.3E−05

1.0E−03

8.2E−03

4.9E−02

Fiber intake

1.3E−03

3.9E−02

1.5E−01

8.9E−01

WCRF/AICR score

8.3E−04

2.4E−02

5.0E−01

1.0E+00

Age at blood collection

8.6E−04

2.6E−022.3E−04

1.1E−03

Alcohol consumption at recruitment

2.8E−04

8.0E−032.0E−01

1.0E+00

Alcohol consumption (lifetime)

8.6E−04

2.6E−02

3.5E−01

1.0E+00

Carbohydrate intake

3.8E−05

1.0E−03

3.0E−01

1.0E+00

Total sugar intake

2.1E−04

6.0E−03

1.1E−01

5.5E−01

Age at blood collection

1.0E−03

4.3E−02

1.2E−01

2.4E−01BMI

8.9E−04

3.6E−02

6.7E−02

1.3E−01

Smoking status

1.2E−04

1.0E−03

7.5E−01

1.0E+00

Smoking status and intensity

2.0E−04

3.0E−03

8.4E−01

1.0E+00

BMI

5.7E−1045.7E−10

6.6E−05

7.9E−04

Waist circumference

2.6E−14

2.6E−143.0E−07

3.6E−06

Hip circumference

4.3E−05

4.3E−05

2.2E−02

2.6E−01

Waist/hip ratio

5.0E−125.0E−12

3.3E−073.9E−06

Carbohydrate intake

4.3E−06

4.3E−06

4.2E−02

5.0E−01

Total sugar intake

1.4E−03

3.2E−02

9.8E−01

1.0E+00

Total fat intake

2.2E−092.2E−09

1.4E−04

1.7E−03

Fatty acids, total saturated intake

6.8E−10

6.8E−10

8.7E−07

1.0E−05

Fatty acids, total monounsaturated intake

1.5E−04

1.0E−03

1.2E−01

1.0E+00

Modified Mediterranean diet score

5.1E−04

1.5E−02

9.5E−03

1.1E−01

Smoking status

2.1E−04

6.0E−03

6.1E−01

1.0E+00

Smoking status and intensity

2.4E−04

8.0E−03

9.0E−01

1.0E+00

Alcohol consumption at recruitment

4.4E−08

4.4E−08

1.4E−04

1.7E−03

Alcohol consumption (lifetime)

1.3E−06

1.3E−06

6.8E−04

8.1E−03

BMI

4.4E−13

4.4E−137.0E−09

8.4E−08

Waist circumference

2.1E−19

2.1E−19

1.8E−122.2E−11

Hip circumference

1.1E−07

1.1E−077.7E−05

9.3E−04

Waist/hip ratio

9.1E−15

9.1E−15

7.0E−10

8.4E−09

Total fat intake

1.4E−11

1.4E−11

2.1E−05

2.5E−04

Fatty acids, total saturated intake

2.2E−17

2.2E−17

1.6E−10

1.9E−09

Healthy Lifestyle Index

2.8E−05

2.0E−03

2.3E−04

2.8E−03

WCRF/AICR score

3.9E−05

2.0E−03

8.1E−05

9.7E−04

Smoking status

3.8E−04

7.0E−03

8.1E−01

1.0E+00

Smoking status and intensity

1.4E−03

2.8E−02

8.6E−01

1.0E+00

BMI

1.9E−07

1.9E−072.2E−03

2.7E−02

Waist circumference

7.2E−12

7.2E−12

7.3E−05

8.8E−04

Hip circumference

4.7E−04

8.0E−03

6.8E−02

8.1E−01

Arginineb
Asparagine

Discovery-

His et al. BMC Medicine(2021) 19:312

Page 9 of 15

Table 2 P-values for associations between metabolites and selecteda variables (Continued)
Metabolite

PC ae C38:2

Variable

Discovery

Validation

P-valueminP P-value

P-value

Bonf. P-value

Waist/hip ratio

1.5E−10

1.5E−10

8.2E−05

9.8E−04

Carbohydrate intake

1.9E−04

5.0E−03

2.2E−01

1.0E+00

Total fat intake

8.5E−08

8.5E−08

4.1E−03

4.9E−02

Fatty acids, total saturated intake

1.5E−06

1.5E−06

1.6E−032.0E−02

Fatty acids, total monounsaturated intake

2.8E−05

2.8E−05

5.0E−02

6.0E−01

Fatty acids, total trans intake

1.4E−04

3.0E−03

2.1E−01

1.0E+00

Modified Mediterranean diet score

3.0E−04

6.0E−03

5.6E−02

6.7E−01

Alcohol consumption at recruitment

3.4E−04

2.4E−02

1.2E−02

9.9E−02

BMI

6.2E−11

6.2E−11

1.9E−09

1.5E−08

Waist circumference

1.6E−13

1.6E−13

2.5E−112.0E−10

Hip circumference

7.6E−08

7.6E−08

1.2E−06

9.6E−06

Waist/hip ratio

1.1E−07

1.1E−07

3.6E−06

2.9E−05

Total fat intake

2.5E−04

1.7E−02

9.2E−03

7.3E−02

Fatty acids, total saturated intake

1.7E−06

1.7E−06

1.2E−04

1.0E−03

Fatty acids, total trans-polyenoic intake

1.2E−03

4.9E−02

6.3E−01

1.0E+00

Bold lines correspond to variable showing significant association after adjustment of P-value for multiple tests in both discovery and validation sets. Models were
adjusted for center of recruitment, age, menopausal status (premenopausal, perimenopausal, postmenopausal), phase of the menstrual cycle for premenopausal
women (follicular, ovulatory, luteal, missing), time of the day, and fasting status at blood collection (no, in between, yes)
Abbreviations: AICR American Institute for Cancer Research, BMI body mass index, C2 acetylcarnitine, PC aa phosphatidylcholine diacyl, PC ae phosphatidylcholineacyl-alkyl, WCRF World Cancer Research Funda
Only associations for which a significant P-value was detected after correction of P-values for multiple tests in the discovery sets are includedb
No association was detected in the discovery set

to those observed in the whole dataset, although sometimes not significant in the validation set (data notshown), such as asparagine and hip circumference andwaist/hip ratio, and PC ae C36:3 and BMI and total andsaturated fat intakes.

Discussion
In this study, we identified several lifestyle and anthropometric correlates of blood metabolites which havebeen previously associated with breast cancer risk in
women not taking exogenous hormones at blood collection. Concentrations of PCs ae C34:2, ae C36:2, ae C36:
3, and ae C38:2 showed negative associations with adiposity and positive associations with total (except for PC
ae C38:2) and saturated fat intakes. PC ae C36:2 also
showed a negative association with alcohol consumption
and positive associations with the WCRF/AICR score
and the Healthy Lifestyle Index. Asparagine concentrations were negatively associated with adiposity, and arginine concentrations were not associated with any ofthe variables examined. Acetylcarnitine concentrations
were positively associated with age but not with any of
the other factors. We did not identify any correlate of
the only diacyl PC (PC aa C36:3) associated with breast
cancer risk. These associations were consistent acrossdifferent BMI, fasting status, and menopausal status
categories.

Acyl-alkyl phosphatidylcholines have been previously
associated with various lifestyle and dietary factors. In
our work, concentrations of acyl-alkyl PCs were negatively associated with measures of adiposity (including
BMI and waist circumference). This observation isconsistent with the global pattern of negative associations between PCs ae and BMI previously reported in
EPIC [12], in particular for PCs ae C38:2 [29] and ae
C36:2 [30], and in the EPIC-Potsdam sub-cohort [31].
PC ae C38:2 and C34:2 were also associated withweight loss in an intervention study (n = 17 participants) [12]. In the EPIC-Potsdam sub-cohort [32], anegative association of several PCs ae was reportedwith risk of type 2 diabetes, as well as a positive correlation with circulating high-density lipoprotein cholesterol. In an analysis of two studies of Japanese andAmerican men and women [33], PCs ae C34:2, 36:3,
and 38:2 were negatively associated with metabolic
syndrome (in particular with high-density lipoproteincholesterol and triglycerides), but not with elevatedwaist circumference. Among 200 Canadian adults
younger than 55 years, concentrations of PCs ae C34:
2, C36:2, and C36:3 were lower in obese participants
with metabolic syndrome than in obese participants
without metabolic syndrome and in normal weightparticipants [34], while an opposite trend was reported for several PCs aa. These results support an

His et al. BMC Medicine(2021) 19:312

Page 10 of 15

Fig. 2 Adjusted means of metabolite concentrations by categories of correlates (N = 2358). Only metabolites and variables for which a significant
association was detected in the discovery and validation sets are shown. Adjusted means and their 95% confidence intervals were obtained fromlinear regression models adjusted for fasting status, center, age, date and time at blood collection, menopausal status, and phase of menstrual
cycle at blood collection. Dotted lines indicate the overall means of metabolite concentration. *Residuals on total energy intake. Abbreviations:Asn, asparagine; AICR, American Institute for Cancer Research; BMI, body mass index; C2, acetylcarnitine; PC aa, phosphatidylcholine diacyl; PC ae,
phosphatidylcholine acyl-alkyl; WCRF, World Cancer Research Fundassociation of PCs with obesity or metabolic healththat deserves further investigation.
Lower concentrations of PCs were reported in vegetarian and vegan men than in meat eaters [35]. Moreover,analyses in colorectal cancer patients (60% males) indicated positive associations of several PCs, mostly acylalkyl, with Western and carnivore dietary patterns [36].
These results are consistent with the positive association
we report with saturated fat intake. However, few studies
have been conducted in women, and an analysis conducted among healthy participants from the KarMeNstudy, not using exogenous hormones, suggested differences in plasma concentrations of some PCs between
men and women, although PCs were not the most important components for predicting sex [37]. A recent
metabolomic study of plasma lipid-related profiles anddiet quality in the Nurses’ Health Study [38] reported
that PC C36:2 plasmalogen was associated with unhealthy components of the Alternate Healthy Eating
Index.
A negative association of PCs and alcohol consumption, in particular PC ae C36:2, has been reported in

EPIC, in both men and women [39]. A negative association with PC ae C36:2 was also observed separately in
men and women from the KORA F4 study when comparing moderate-to-heavy drinkers (≥20 g/day for
women, 40 g/day in men) with light drinkers (< 20 g/day
for women, 40 g/day in men) [40], and in the CARLA
study (men and women combined) [41].
The positive associations reported between PC ae C36:
2 and the WCRF/AICR and HLI scores, which integrate
alcohol and body weight components, likely reflect inverse associations of this metabolite with alcohol consumption and adiposity as demonstrated in the analysesof single correlates. These associations are in line with arecent study conducted in EPIC on metabolic signaturesof a healthy lifestyle, assessed by the WCRF/AICR score[42]. In this work, PCs ae 36:2 and C38:2 were among
endogenous metabolites with the greatest loadings (>100 examined) in the signature of the WCRF/AICR
score. This metabolic signature showed the greatest correlations with the recommendations regarding normal
weight maintenance and alcohol avoidance, in line withthe associations we report. In contrast, a study inHis et al. BMC Medicine(2021) 19:312

colorectal cancer patients indicated negative associations
between several PCs ae and aa and the WCRF/AICR
score [36]. However, the score was restricted to its dietary components, therefore not considering the bodyweight component.
Metabolomics studies on aging reported increasing
circulating concentrations of acylcarnitines, mostly longchain, with age [43, 44], which could reflect loss in mitochondrial function [45]. In a study [46] comparing metabolites in serum samples obtained 7 years apart fromthe same individuals (KORA S4 and KORA F4), acetylcarnitine and several other acylcarnitines increased in
the follow-up samples compared with baseline samples.
Associations of similar direction were observed in theirvalidation study on samples collected 4 years apart, although not statistically significant after accounting for
multiple testing. Acylcarnitines have also been associatedwith impaired glucose metabolism and insulin resistance,
but these associations were most often reported forlong-chain or odd short-chain acylcarnitines [47–51], although associations with acetylcarnitine (which is aneven short-chain acylcarnitine) have also been reported[52]. In our previous work, this metabolite was the only
one to show a positive association with breast cancer
risk in age-matched cases and controls, suggesting thatits association with age does not fully explain the association with breast cancer. In the present work, we did notobserve any association of acetylcarnitine with anthropometric factors likely associated with metabolic health, incontrast with a positive association with BMI reported
in the EPIC Norfolk cohort [53].
A negative association between circulating asparagine
and obesity has been recently reported in different populations, including Europeans [50, 53], obese Iranian
adults [54], and Japanese [55]. Negative associations withdiabetes and coronary artery disease have also been reported [50, 53], in lean as well as in obese subjects [49].
However, most studies exploring the associations between amino acids and obesity showed significant associations only with branched-chain amino acids (which do
not include asparagine) [49, 56]. Asparagine was alsopart of the metabolic signature of a healthy lifestyle derived in EPIC [42] and of the metabolic signature of
BMI, waist circumference, and waist/hip ratio [12].
In our study, arginine was not associated with any of
the factors investigated. This result contrasts with thosein several studies reporting negative associations of arginine with age [46] and with obesity and alcohol intake,as well as a positive association with smoking in the
EPIC Norfolk cohort [53], which however had not excluded hormone users. Arginine has also been negatively
associated with hemoglobin concentrations and withinsulin-like growth factor 1 and estradiol [57] in premenopausal women not using exogenous hormones.

Page 11 of 15

These observations may suggest that arginine concentrations could potentially be more tightly regulated by endogenous metabolism compared to lifestyle exposures.
Major strengths of this work include the wide variety of data collected which enabled us to investigate
many potential correlates for the metabolites associated with breast cancer risk, and the large sample sizeof our study, compared to other metabolomics studies, where large studies are essential [58]. With thedetailed information available on characteristics ofwomen at blood collection, we were also able to exclude hormone users from our analysis, which isimportant as hormone use could possibly affect concentrations of some metabolites [59].
A first limitation to this work is the cross-sectional design, which prevents us from drawing any conclusions
on the timing or causality of the associations. Anotherlimitation is that the large sample size was achieved bypooling data from different previous studies, rather thanby initial design, therefore adding methodological complexity because of analyses performed by different laboratories, with different instruments, and on different
biological matrices. However, the analytical protocol
used has shown high inter-laboratory reproducibility[60], and we addressed potential heterogeneity in metabolite concentrations by developing a dedicated pipeline[24] applied to the data prior to statistical analyses. In
addition, for all metabolites included (except asparagine,
not evaluated), high correlations were reported between
measures in serum and in plasma (r ≥ 0.78, except forarginine, r = 0.50), although concentrations were generally higher in serum than in plasma, in particular for arginine [61]. Good reliability of measurements was also
reported for both matrices (intra-class correlations forthe metabolites of interest ≥0.58 in plasma, ≥0.67 inserum) [62]. Furthermore, exclusion of serum samples
did not substantially modify the results. A third limitation is the heterogeneity of fasting status of participants.
However, variables to determine fasting status were
carefully recorded, therefore enabling us to test the effect of this variable on the results, and we found no evidence of heterogeneity in the associations by fasting
status. Dietary intakes were assessed using food frequency questionnaires adapted to local habits. These
questionnaires were validated through a calibration approach using a common 24-h diet recall [63] to adjust
for possible systematic misclassification in dietary measurements, and a validation study using 24-h urine samples was conducted [64]. Despite these methodologicalefforts, however, potential measurement error may persist because of recall bias, misreporting of consumption
for certain foods, or errors related to the food composition tables used (despite careful matching [15]). Nevertheless, several cross-sectional studies showing goodHis et al. BMC Medicine(2021) 19:312

correlations [65, 66] between intakes measured by foodquestionnaires and expected specific biomarkers suggestthat data from food frequency questionnaires can beused for the purposes of the present work. Finally, theapplied technology for PC measurement does not allow
for precise identification of the compounds measured,
since the signal observed is not specific and may correspond to different structural isomers. Further work isneeded to investigate specifically associations with lipid
compounds.

Conclusions
In conclusion, this cross-sectional analysis identified several modifiable correlates of blood concentrations of metabolites associated with breast cancer risk. These
associations may indicate possible mechanisms underlying associations between lifestyle and anthropometricfactors, and risk of breast cancer. To better understandhow our results could improve our current knowledge
on the association between lifestyle factors and breast
cancer risk, dedicated tools, such as mediation analysis,bring promising perspectives. Intervention studies would
be required to evaluate the possible causality of the associations observed with modifiable factors and to assess
whether concentrations of these specific metabolites
could be modified through lifestyle changes.
Abbreviations
AICR: American Institute for Cancer Research; Asn: Asparagine; BMI: Body
mass index; C2: Acetylcarnitine; CI: Confidence interval; EPIC: European
Prospective Investigation into Cancer and Nutrition; HLI: Healthy Lifestyle
Index; IARC: International Agency for Research on Cancer; LLOQ: Lower limitof quantification; PC aa: Phosphatidylcholine diacyl; PC
ae: Phosphatidylcholine acyl-alkyl; SD: Standard deviation; WCRF: World
Cancer Research Fund

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12916-021-02183-2.
Additional file 1: Associations between metabolites and all variables
tested as correlates, in discovery set and, for significant associations, invalidation set (Supplementary Table 1); Stratified analyses by BMI,
menopausal status, and fasting status at blood collection, for metabolites
and variables showing a P-int < 0.20 after correction for multiple testing,in discovery set (Supplementary Table 2). Abbreviations: AICR American
Institute for Cancer Research; Asn asparagine; BMI body mass index; C2
acetylcarnitine; HLI Healthy lifestyle index; PC aa phosphatidylcholine diacyl; PC ae phosphatidylcholine acyl-alkyl; SD Standard deviation; SE:
Standard error of estimate; WCRF World Cancer Research Fund. (XLS 177
kb)

Acknowledgements
The authors would like to thank Mr Bertrand Hemon for his support inpreparing the databases, Ms Audrey Gicquiau and Dr David Achaintre for the
analyses of samples in several of the original studies, and all EPIC
participants. The EPIC-Norfolk team thank all the participants who have beenpart of the project and the many members of the study teams at the University of Cambridge who have enabled this research.

Page 12 of 15

IARC disclaimer
Where authors are identified as personnel of the International Agency for
Research on Cancer/World Health Organization, the authors alone areresponsible for the views expressed in this article and they do notnecessarily represent the decisions, policy, or views of the International
Agency for Research on Cancer/World Health Organization.
Authors’ contributions
The authors’ responsibilities were as follows: SR, MH, and VV conceived anddesigned the research; MH analyzed the data with assistance of VV and SR;
SR, MH, VV, LD, JAS, RCT, and MJG were responsible for drafting themanuscript; KO, CK, AT, LL, JAR, GS, TJ, VK, MBS, GM, SS, SP, RT, AM, JMAB,
EMM, KSO, THN, TMS, AA, MJS, PA, SMCY, EA, LV, AW, AKH, EW, and IH
provided the original data, information on the respective populations, andadvice on the study design, analysis, and interpretation of the findings; allauthors provided critical interpretation of the results and review of the firstdraft; all authors read and approved the final manuscript.
Funding
This work was funded by the French National Cancer Institute (grant number
2015-166). Mathilde His’ work reported here was undertaken during the tenure of a postdoctoral fellowship awarded by the International Agency for Research on Cancer, financed by the Fondation ARC.
The coordination of EPIC is financially supported by International Agency for
Research on Cancer (IARC) and also by the Department of Epidemiology andBiostatistics, School of Public Health, Imperial College London, which has
additional infrastructure support provided by the NIHR Imperial Biomedical
Research Centre (BRC).
The national cohorts are supported by Danish Cancer Society (Denmark);
Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale del’Education Nationale, Institut National de la Santé et de la Recherche
Médicale (INSERM) (France); German Cancer Aid, German Cancer ResearchCenter (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke(DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo
and National Research Council (Italy); Dutch Ministry of Public Health, Welfare
and Sports (VWS), LK Research Funds, Dutch Prevention Funds, Dutch ZON(Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF) (TheNetherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII),
Regional Governments of Andalucía, Asturias, Basque Country, Murcia andNavarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer
Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); and Cancer Research UK (14136 to EPIC-Norfolk (DOI
10.22025/2019.10.105.00004); C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143, MR/N003284/1, MC-UU_12015/1 and
MC_UU_00006/1 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (UK). The
funders were not involved in designing the study; collecting, analyzing, orinterpreting the data; or writing or submitting the manuscript for
publication.
Availability of data and materials
EPIC data are available for investigators who seek to answer importantquestions on health and disease in the context of research projects that areconsistent with the legal and ethical standard practices of IARC/WHO andthe EPIC Centres. The primary responsibility for accessing the data belongs
to the EPIC centers that provided them. For information on how to submit
an application for gaining access to EPIC data and/or biospecimens, please
follow the instructions at http://epic.iarc.fr/access/index.php.

Declarations
Ethics approval and consent to participate
This study complies with the Declaration of Helsinki. The study has been
approved by the IARC Ethics Committee (ref IEC 14-02). Ethical review boardsof IARC and all local institutions where participants had been recruited gave
approval for the study, and all participants gave written informed consent
for data collection and storage, as well as individual follow-up.
Consent for publication
Not applicable.

His et al. BMC Medicine(2021) 19:312

Competing interests
Marc J Gunter is an Editorial Adviser for BMC Medicine, and Joseph
A Rothwell is an Editorial Board Member for BMC Medicine.
The other authors declare that they have no competing interests.
Author details
1
International Agency for Research on Cancer (IARC/WHO), Nutrition andMetabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08 Lyon, France.
2
Cancer Epidemiology Unit, Nuffield Department of Population Health,
University of Oxford, Oxford, UK. 3Department of Public Health, Aarhus
University, Aarhus, Denmark. 4Danish Cancer Society Research Center,
Copenhagen, Denmark. 5Department of Public Health, Section ofEnvironmental Health, Faculty of Health and Medical Sciences, University of
Copenhagen, Copenhagen, Denmark. 6Université Paris-Saclay, UVSQ, Inserm,
CESP U1018, “Exposome and Heredity” team, Gustave Roussy, Villejuif, France.
7
Department of Statistics, Computer Science, Applications “G. Parenti”,
University of Florence, Florence, Italy. 8Department of Cancer Epidemiology,
German Cancer Research Center (DKFZ), Heidelberg, Germany. 9Departmentof Molecular Epidemiology, German Institute of Human Nutrition, Nuthetal,
Germany. 10Institute of Nutritional Science, University of Potsdam, Potsdam,
Germany. 11Institute for Cancer Research, Prevention and Clinical Network(ISPRO), Florence, Italy. 12Epidemiology and Prevention Unit, Fondazione IRCC
S Instituto Nazionale dei Tumori di Milano, Milan, Italy. 13Dipartimento Di
Medicina Clinica E Chirurgia, Federico Ii University, Naples, Italy. 14Cancer
Registry and Histopathology Department, Provincial Health Authority (ASP 7)
Ragusa, Ragusa, Italy. 15Department of Clinical and Biological Sciences,
University of Turin, Turin, Italy. 16Center for Nutrition, Prevention, and HealthServices, National Institute for Public Health and the Environment (RIVM),
Bilthoven 3720, BA, the Netherlands. 17Julius Center for Health Sciences andPrimary Care, University Medical Center Utrecht, Utrecht University, Utrecht,
the Netherlands. 18Department of Community Medicine, Faculty of Health
Sciences, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway.
19
Unit of Nutrition and Cancer, Catalan Institute of Oncology - ICO,
L’Hospitalet de Llobregat, Spain. 20Nutrition and Cancer Group;
Epidemiology, Public Health, Cancer Prevention and Palliative Care Program;
Bellvitge Biomedical Research Institute - IDIBELL, L’Hospitalet de Llobregat,
Spain. 21Escuela Andaluza de Salud Pública (EASP), Granada, Spain. 22Institutode Investigación Biosanitaria ibs.GRANADA, Granada, Spain. 23Centro deInvestigación Biomédica en Red de Epidemiología y Salud Pública(CIBERESP), Madrid, Spain. 24Department of Preventive Medicine and Public
Health, University of Granada, Granada, Spain. 25Ministry of Health of theBasque Government, Sub-Directorate for Public Health and Addictions ofGipuzkoa, San Sebastián, Spain. 26Biodonostia Health Research Institute,
Group of Epidemiology of Chronic and Communicable Diseases, San
Sebastián, Spain. 27CIBER Epidemiology and Public Health (CIBERESP),
Instituto de Salud Carlos III (ISCIII), Madrid, Spain. 28Department of
Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
29
Research Group on Demography and Health, National Faculty of Public
Health, University of Antioquia, Medellín, Colombia. 30Navarra Public HealthInstitute, Pamplona, Spain. 31IdiSNA, Navarra Institute for Health Research,
Pamplona, Spain. 32Department of Radiation Sciences, Oncology, UmeåUniversity, Umeå, Sweden. 33Sustainable Health, Department of Public Healthand Clinical Medicine, Umeå University, Umeå, Sweden. 34Department of
Epidemiology and Biostatistics, School of Public Health, Imperial College
London, London, UK. 35International Agency for Research on Cancer (IARC/WHO), Office of the Director, Lyon, France.

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Received: 9 July 2021 Accepted: 9 November 2021
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